Physical surfaces such as metal, plastic, and paper possess different optical qualities that lead to different characteristics in images.
We have found that humans can effectively estimate certain surface reflectance properties from a single image without knowledge of
illumination. We develop a machine vision system to perform similar reflectance estimation tasks automatically.
The problem of estimating reflectance from single images under unknown, complex illumination
proves highly underconstrained due to the variety of potential reflectances and illuminations.
Our solution relies on statistical regularities in the spatial structure of real-world illumination.
These regularities translate into predictable relationships between surface reflectance and certain statistical features of the image.
We determine these relationships using machine learning techniques. Our algorithms do not depend on color or polarization; they apply even to
monochromatic imagery. An ability to estimate reflectance under uncontrolled illumination will further efforts to recognize materials
and surface properties, to capture computer graphics models from photographs, and to generalize classical motion and stereo algorithms
such that they can handle non-Lambertian surfaces.